Data Exploration of Framingham Heart Study Teaching Dataset¶
Please note: This notebook uses open access data
Please note: BioDataCatalyst RAS Login in the BRH Profile Page needs to be authorized
This teaching dataset was developed using the longitudinal Framingham heart study as the data source. The teaching dataset includes three clinical examination and 20 year follow-up data based on a subset of the original Framingham cohort participants. This dataset was created for teaching and training purposes, and certain measures were used to created anonymous versions. Detailed documentation on variables can be found HERE
In this tutorial, we will demonstrate how to pull the object file of Framingham teaching dataset from BioData Catalyst data commons into a BRH workspace, and perform data exploration and visualization using Python packages.
Import Python libraries¶
In [1]:
import pandas as pd
import numpy as np
import pyreadstat
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
pd.set_option('mode.chained_assignment', None)
Pull Framingham data file¶
In [2]:
!gen3 --commons_url gen3.biodatacatalyst.nhlbi.nih.gov drs-pull object dg.4503/c97507dd-bb79-45ce-9186-3d14ad823f81
fram_df, fram_meta = pyreadstat.read_sav("frmgham2.sav", apply_value_formats=True)
{"succeeded": ["dg.4503/c97507dd-bb79-45ce-9186-3d14ad823f81"], "failed": []}
Data exploration¶
- Display column labels and their full names from metadata
In [3]:
col_names = fram_meta.column_names_to_labels
print ("{:<10} {:<10}".format('LABEL', 'NAME'))
# print each column item.
for key, value in col_names.items():
print ("{:<10} {:<10}".format(key, value))
LABEL NAME RANDID Random ID SEX SEX TOTCHOL Serum Cholesterol mg/dL AGE Age (years) at examination SYSBP Systolic BP mmHg DIABP Diastolic BP mmHg CURSMOKE Current Cig Smoker Y/N CIGPDAY Cigarettes per day BMI Body Mass Index (kg/(M*M) DIABETES Diabetic Y/N BPMEDS Anti-hypertensive meds Y/N HEARTRTE Ventricular Rate (beats/min) GLUCOSE Casual Glucose mg/dL EDUC 0-11 years, HS or GED, Some Coll, Coll Grad+ PREVCHD Prevalent CHD (MI,AP,CI) PREVAP Prevalent Angina PREVMI Prevalent MI (Hosp,Silent) PREVSTRK Prevalent Stroke (Infarct,Hem) PREVHYP Prevalent Hypertension TIME Days since Index Exam PERIOD Examination cycle HDLC HDL Cholesterol mg/dL LDLC LDL Cholesterol mg/dL DEATH Death indicator ANGINA Incident Angina Pectoris HOSPMI Incident Hospitalized MI MI_FCHD Incident Hosp MI-Fatal CHD ANYCHD Incident Hosp MI, AP, CI, Fatal CHD STROKE Incident Stroke Fatal/non-fatal CVD Incident Hosp MI or Stroke, Fatal or Non HYPERTEN Incident Hypertension TIMEAP Days Baseline-Inc Angina TIMEMI Days Baseline-Inc Hosp MI TIMEMIFC Days Baseline-Inc MI-Fatal CHD TIMECHD Days Baseline-Inc Any CHD TIMESTRK Days Baseline-Inc Stroke TIMECVD Days Baseline-Inc CVD TIMEDTH Days Baseline-Death TIMEHYP Days Baseline-Inc Hypertension
- Explore the demographic characteristics of participants
In [4]:
# change the patiant ID column type from numeric to string
fram_df["RANDID"] = fram_df["RANDID"].astype(str)
fram_df["RANDID"] = fram_df["RANDID"].str[:-2]
In [5]:
# Map the gender value to male an female
sex_group_list = ['Male' if i==1.0 else 'Female' for i in fram_df['SEX']]
fram_df['SEX_GROUP'] = sex_group_list
In [6]:
# Create a subset of participants from first visit
visit_1 = fram_df[fram_df['PERIOD']==1]
visit_1.head()
Out[6]:
| RANDID | SEX | TOTCHOL | AGE | SYSBP | DIABP | CURSMOKE | CIGPDAY | BMI | DIABETES | ... | HYPERTEN | TIMEAP | TIMEMI | TIMEMIFC | TIMECHD | TIMESTRK | TIMECVD | TIMEDTH | TIMEHYP | SEX_GROUP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2448 | 1.0 | 195.0 | 39.0 | 106.0 | 70.0 | 0.0 | 0.0 | 26.97 | 0.0 | ... | 0.0 | 8766.0 | 6438.0 | 6438.0 | 6438.0 | 8766.0 | 6438.0 | 8766.0 | 8766.0 | Male |
| 2 | 6238 | 2.0 | 250.0 | 46.0 | 121.0 | 81.0 | 0.0 | 0.0 | 28.73 | 0.0 | ... | 0.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | Female |
| 5 | 9428 | 1.0 | 245.0 | 48.0 | 127.5 | 80.0 | 1.0 | 20.0 | 25.34 | 0.0 | ... | 0.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | Male |
| 7 | 10552 | 2.0 | 225.0 | 61.0 | 150.0 | 95.0 | 1.0 | 30.0 | 28.58 | 0.0 | ... | 1.0 | 2956.0 | 2956.0 | 2956.0 | 2956.0 | 2089.0 | 2089.0 | 2956.0 | 0.0 | Female |
| 9 | 11252 | 2.0 | 285.0 | 46.0 | 130.0 | 84.0 | 1.0 | 23.0 | 23.10 | 0.0 | ... | 1.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 4285.0 | Female |
5 rows × 40 columns
In [7]:
# Functions to categorize age, BMI, and education value into groups
# Define age groups within participants
def age_group(agelist):
min_age = min(agelist)
grouplabel1 = str(min_age) + "-40 yr"
grouplabel2 = "41-50 yr"
grouplabel3 = "51-60 yr"
grouplabel4 = "61-70 yr"
grouplabel5 = "71-80 yr"
grouplabel6 = "81-90 yr"
grouplist = []
for i in agelist:
if i <= 40:
grouplist.append(grouplabel1)
elif i <=50:
grouplist.append(grouplabel2)
elif i <= 60:
grouplist.append(grouplabel3)
elif i <= 70:
grouplist.append(grouplabel4)
elif i <= 80:
grouplist.append(grouplabel5)
elif i <= 90:
grouplist.append(grouplabel6)
else:
grouplist.append("NA")
return grouplist
# Define BMI groups
def bmi_group(bmilist):
grouplabel1 = "underweight"
grouplabel2 = "healthy"
grouplabel3 = "overweight"
grouplabel4 = "obese"
grouplist = []
for i in bmilist:
if i < 18.5 :
grouplist.append(grouplabel1)
elif i < 25:
grouplist.append(grouplabel2)
elif i < 30:
grouplist.append(grouplabel3)
else:
grouplist.append(grouplabel4)
return grouplist
# Define education groups
def edu_group(edu_list):
grouplabel1 = "0-11 years"
grouplabel2 = "High School Diploma, GED"
grouplabel3 = "Some College, Vocational School"
grouplabel4 = "College (BS, BA) degree or more"
grouplist = []
for i in edu_list:
if i == 1.0:
grouplist.append(grouplabel1)
elif i == 2.0:
grouplist.append(grouplabel2)
elif i == 3.0:
grouplist.append(grouplabel3)
elif i == 4.0:
grouplist.append(grouplabel4)
else:
grouplist.append("NA")
return grouplist
In [8]:
visit_1["AGE_GROUP"] = age_group(list(visit_1["AGE"]))
visit_1["BMI_GROUP"] = bmi_group(list(visit_1["BMI"]))
visit_1["EDUC_GROUP"] = edu_group(list(visit_1["EDUC"]))
visit_1[["AGE", "AGE_GROUP", "BMI", "BMI_GROUP", 'EDUC', 'EDUC_GROUP']].head(5)
Out[8]:
| AGE | AGE_GROUP | BMI | BMI_GROUP | EDUC | EDUC_GROUP | |
|---|---|---|---|---|---|---|
| 0 | 39.0 | 32.0-40 yr | 26.97 | overweight | 4.0 | College (BS, BA) degree or more |
| 2 | 46.0 | 41-50 yr | 28.73 | overweight | 2.0 | High School Diploma, GED |
| 5 | 48.0 | 41-50 yr | 25.34 | overweight | 1.0 | 0-11 years |
| 7 | 61.0 | 61-70 yr | 28.58 | overweight | 3.0 | Some College, Vocational School |
| 9 | 46.0 | 41-50 yr | 23.10 | healthy | 3.0 | Some College, Vocational School |
In [9]:
# Count the number of participants based on group category
pt_sex_age = pd.crosstab(index=visit_1['AGE_GROUP'], columns=visit_1['SEX_GROUP'])
pt_sex_bmi = pd.crosstab(index=visit_1['BMI_GROUP'], columns=visit_1['SEX_GROUP'])
pt_sex_edu = pd.crosstab(index=visit_1['EDUC_GROUP'], columns=visit_1['SEX_GROUP'])
pd.concat([pt_sex_age, pt_sex_bmi, pt_sex_edu], keys=['age', 'bmi', 'education'])
Out[9]:
| SEX_GROUP | Female | Male | |
|---|---|---|---|
| age | 32.0-40 yr | 415 | 339 |
| 41-50 yr | 908 | 731 | |
| 51-60 yr | 795 | 584 | |
| 61-70 yr | 372 | 290 | |
| bmi | healthy | 1233 | 703 |
| obese | 359 | 237 | |
| overweight | 853 | 992 | |
| underweight | 45 | 12 | |
| education | 0-11 years | 979 | 843 |
| College (BS, BA) degree or more | 206 | 296 | |
| High School Diploma, GED | 772 | 509 | |
| NA | 56 | 57 | |
| Some College, Vocational School | 477 | 239 |
In [10]:
# Plot the counts of these demographic characteristics among participants
# Reformat the dataframe before plotting
demo_df = pd.concat([pt_sex_age, pt_sex_bmi, pt_sex_edu], keys=['Age','BMI','Education'])
demo_df.reset_index(inplace=True)
demo_df = pd.melt(demo_df, id_vars=['level_0', 'level_1'], value_vars=['Female', 'Male'], ignore_index=False)
demo_df = demo_df.rename({'level_0':"Group type", 'level_1':'Group value', 'value':'Counts', 'SEX_GROUP':'Sex'}, axis="columns")
# Show bar plot of participant counts based on gender, age, BMI, and education group
demo_fig = px.bar(demo_df, x="Group value", y="Counts", color="Sex", facet_row="Group type",
width=900, height=700, title="Age Group, BMI, and Education Characteristics of Participants in the Framingham Heart Study")
demo_fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
demo_fig.update_xaxes(matches='x')
demo_fig.show()
Demographic characteristics of FHS participants:
- Most participants in the Framingham Heart study were recruited to the study from age group of 40-50 and 50-60.
- At first visit, few participants were found at obese and underweight BMI groups. The majority participants were found in either healthy or overweight BMI group. We saw more females in the healthy BMI group than males(1,233 vs 703).
- Nearly three quarters of the participants were found with 0-11 years educations or GED. Fewer participants were found with college degree.
Risk factor exploration¶
- Risk factor at different time points
In [11]:
# add bmi group, age group, and education group to the fram_df
fram_df["AGE_GROUP"] = age_group(list(fram_df["AGE"]))
fram_df["BMI_GROUP"] = bmi_group(list(fram_df["BMI"]))
fram_df["EDUC_GROUP"] = edu_group(list(fram_df["EDUC"]))
fram_df.head(5)
Out[11]:
| RANDID | SEX | TOTCHOL | AGE | SYSBP | DIABP | CURSMOKE | CIGPDAY | BMI | DIABETES | ... | TIMEMIFC | TIMECHD | TIMESTRK | TIMECVD | TIMEDTH | TIMEHYP | SEX_GROUP | AGE_GROUP | BMI_GROUP | EDUC_GROUP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2448 | 1.0 | 195.0 | 39.0 | 106.0 | 70.0 | 0.0 | 0.0 | 26.97 | 0.0 | ... | 6438.0 | 6438.0 | 8766.0 | 6438.0 | 8766.0 | 8766.0 | Male | 32.0-40 yr | overweight | College (BS, BA) degree or more |
| 1 | 2448 | 1.0 | 209.0 | 52.0 | 121.0 | 66.0 | 0.0 | 0.0 | NaN | 0.0 | ... | 6438.0 | 6438.0 | 8766.0 | 6438.0 | 8766.0 | 8766.0 | Male | 51-60 yr | obese | College (BS, BA) degree or more |
| 2 | 6238 | 2.0 | 250.0 | 46.0 | 121.0 | 81.0 | 0.0 | 0.0 | 28.73 | 0.0 | ... | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | Female | 41-50 yr | overweight | High School Diploma, GED |
| 3 | 6238 | 2.0 | 260.0 | 52.0 | 105.0 | 69.5 | 0.0 | 0.0 | 29.43 | 0.0 | ... | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | Female | 51-60 yr | overweight | High School Diploma, GED |
| 4 | 6238 | 2.0 | 237.0 | 58.0 | 108.0 | 66.0 | 0.0 | 0.0 | 28.50 | 0.0 | ... | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | 8766.0 | Female | 51-60 yr | overweight | High School Diploma, GED |
5 rows × 43 columns
In [12]:
# extract a subset of data for risk factor exploration
rf_df = fram_df[['RANDID','SEX_GROUP', 'TIME', 'PERIOD', 'BMI', 'AGE',
'SYSBP', 'DIABP', 'CIGPDAY', 'TOTCHOL', 'HDLC',
'LDLC', 'GLUCOSE']]
In [13]:
# Unpivot a dataframe from wide to long format
rf_df_melt = pd.melt(rf_df, id_vars=['RANDID','SEX_GROUP','TIME', 'PERIOD'],
value_vars=['BMI', 'AGE', 'SYSBP', 'DIABP', 'CIGPDAY',
'TOTCHOL', 'HDLC', 'LDLC', 'GLUCOSE'])
rf_df_melt.head()
Out[13]:
| RANDID | SEX_GROUP | TIME | PERIOD | variable | value | |
|---|---|---|---|---|---|---|
| 0 | 2448 | Male | 0.0 | 1.0 | BMI | 26.97 |
| 1 | 2448 | Male | 4628.0 | 3.0 | BMI | NaN |
| 2 | 6238 | Female | 0.0 | 1.0 | BMI | 28.73 |
| 3 | 6238 | Female | 2156.0 | 2.0 | BMI | 29.43 |
| 4 | 6238 | Female | 4344.0 | 3.0 | BMI | 28.50 |
The next block shows the distribution of several risk factor variables, including BMI and AGE, at three visits.
In [14]:
# boxplot of risk factors at 3 visits
rf_fig1 = px.violin(data_frame=rf_df_melt.loc[rf_df_melt['variable'].isin(['AGE', 'BMI']),],
x='PERIOD', y='value', width=800, height=500, color='variable', box=True)
rf_fig1.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
rf_fig1.update_xaxes(title="Visit")
rf_fig1.show()
- The median age of participants at three visits were 49, 55 and 60, respectively.
- At all three visit time point, the median value of BMIs were found around 25.5, which falls into
overweight category.
The next block shows the distribution of several variables, including Systolic Blood Pressure (SYSBP), Diastolic Blood Pressure (DIABP), Serum Total Cholesterol (TOTCHOL), and Casual Serum Glucose (GLUCOSE), at three visits.
In [15]:
# boxplot of few risk factors at three visits
rf_fig2 = px.box(data_frame=rf_df_melt.loc[rf_df_melt['variable'].isin(['SYSBP', 'DIABP',
'TOTCHOL','GLUCOSE']),],
x='PERIOD', y='value', width=950, height=500, color='variable')
rf_fig2.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
rf_fig2.update_xaxes(title="Visit")
rf_fig2.show()
- Risk factors and events heatmap visualization
The next block categorizes some of the risk factor values into binary groups using a threshold. For instance, Age over 60 is considered as a risk factor.
In [16]:
# Extract only the final visit of each participant and
# and known risk factors asscoiated with cardiovascular disease incidence
visit_final = fram_df.drop_duplicates("RANDID", keep='last')
visit_rf = visit_final[['RANDID','AGE', 'SEX_GROUP','CURSMOKE', 'TOTCHOL', 'BMI',
'GLUCOSE', 'PREVCHD', 'PREVSTRK', 'PREVHYP', 'DIABETES', 'HEARTRTE']]
visit_rf['TOTCHOL_RF'] = [1 if i>200 else 0 for i in visit_rf['TOTCHOL']]
visit_rf['BMI_RF'] = [1 if i>25 else 0 for i in visit_rf['BMI']]
visit_rf['GLUCOSE_RF'] = [1 if i>200 else 0 for i in visit_rf['GLUCOSE']]
visit_rf['HEARTRTE_RF'] = [1 if i>100 else 0 for i in visit_rf['HEARTRTE']]
visit_rf['AGE_RF'] = [1 if i>=60 else 0 for i in visit_rf['AGE']]
visit_rf = visit_rf.drop(['AGE','TOTCHOL','BMI','GLUCOSE', 'HEARTRTE', 'SEX_GROUP'], axis=1)
visit_rf.head()
Out[16]:
| RANDID | CURSMOKE | PREVCHD | PREVSTRK | PREVHYP | DIABETES | TOTCHOL_RF | BMI_RF | GLUCOSE_RF | HEARTRTE_RF | AGE_RF | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2448 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 0 | 0 | 0 | 0 |
| 4 | 6238 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 1 | 0 | 0 | 0 |
| 6 | 9428 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 1 | 0 | 0 | 0 |
| 8 | 10552 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1 | 1 | 0 | 0 | 1 |
| 11 | 11252 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0 | 0 | 0 | 0 | 0 |
The next block combines the risk factor dataframe with disease event and generates a correlation heatmap with these variables
In [17]:
# combine the visit_rf with event data
visit_event = visit_final[['RANDID', 'ANGINA','HOSPMI','MI_FCHD','ANYCHD', 'STROKE', 'CVD', 'HYPERTEN','DEATH']]
visit_rf_event = visit_rf.merge(visit_event, left_on="RANDID", right_on="RANDID")
visit_rf_event.head(5)
Out[17]:
| RANDID | CURSMOKE | PREVCHD | PREVSTRK | PREVHYP | DIABETES | TOTCHOL_RF | BMI_RF | GLUCOSE_RF | HEARTRTE_RF | AGE_RF | ANGINA | HOSPMI | MI_FCHD | ANYCHD | STROKE | CVD | HYPERTEN | DEATH | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2448 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 0 | 0 | 0 | 0 | 0.0 | 1.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 1 | 6238 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 1 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 9428 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 1 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 10552 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1 | 1 | 0 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 4 | 11252 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
In [18]:
# generate correlation heatmap
visit_rf_event_noid = visit_rf_event.drop(['RANDID'],axis=1)
sns.heatmap(visit_rf_event_noid.corr())
Out[18]:
<Axes: >
In [19]:
# create a column that combines all the risk factors
visit_rf_event['RF_SUM'] = visit_rf_event['CURSMOKE'] + visit_rf_event['PREVCHD'] + visit_rf_event['PREVSTRK'] + visit_rf_event['PREVHYP']+ visit_rf_event['DIABETES'] + visit_rf_event['TOTCHOL_RF']+ visit_rf_event['BMI_RF'] + visit_rf_event['GLUCOSE_RF'] + visit_rf_event['HEARTRTE_RF'] + visit_rf_event['AGE_RF']
In [20]:
visit_rf_event.tail(5)
Out[20]:
| RANDID | CURSMOKE | PREVCHD | PREVSTRK | PREVHYP | DIABETES | TOTCHOL_RF | BMI_RF | GLUCOSE_RF | HEARTRTE_RF | AGE_RF | ANGINA | HOSPMI | MI_FCHD | ANYCHD | STROKE | CVD | HYPERTEN | DEATH | RF_SUM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4429 | 9990894 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1 | 0 | 0 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 4.0 |
| 4430 | 9993179 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 3.0 |
| 4431 | 9995546 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1 | 0 | 0 | 0 | 0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 2.0 |
| 4432 | 9998212 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0 | 1 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 |
| 4433 | 9999312 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 |
The next two blocks generate a counts table between risk factor sum variable and event sum variable
In [21]:
visit_rf_event['EVENT_SUM'] = visit_rf_event['ANYCHD'] + visit_rf_event['STROKE'] + visit_rf_event['CVD'] + visit_rf_event['HYPERTEN']
In [22]:
rf_count = pd.crosstab(index=visit_rf_event['RF_SUM'], columns=visit_rf_event['EVENT_SUM'])
rf_count
Out[22]:
| EVENT_SUM | 0.0 | 1.0 | 2.0 | 3.0 | 4.0 |
|---|---|---|---|---|---|
| RF_SUM | |||||
| 0.0 | 60 | 15 | 1 | 0 | 0 |
| 1.0 | 250 | 152 | 19 | 14 | 2 |
| 2.0 | 413 | 472 | 77 | 56 | 4 |
| 3.0 | 203 | 741 | 123 | 233 | 28 |
| 4.0 | 31 | 552 | 105 | 297 | 52 |
| 5.0 | 0 | 114 | 73 | 169 | 44 |
| 6.0 | 0 | 13 | 20 | 41 | 32 |
| 7.0 | 0 | 2 | 1 | 11 | 10 |
| 8.0 | 0 | 0 | 0 | 2 | 2 |
The next block creates a histogram showing the composition of disease events in each risk factor group.
In [23]:
rf_count.reset_index(inplace=True)
rf_count_melt = pd.melt(rf_count, id_vars=['RF_SUM'], value_vars=[0,1,2,3,4], ignore_index=False)
rf_event_fig = px.bar(rf_count_melt, x='RF_SUM', y='value',
color='EVENT_SUM', title="Risk Factors and Disease Events Histogram ",
color_discrete_sequence=px.colors.sequential.Sunsetdark)
rf_event_fig.show()
- Most of the participants had at least two risk fastors at third visit.
- With the increase of risk factor, we observed a significant increase in multiple disease events. For instance, the majority of participants with three risk factors (the tallest bar) have at least 1 disease event.
- Among the participants with at least 3 risk factors, there is a signidicant increase in multiple disease event (2, 3, or 4). Meanwhile, fewer or no participants with at least 3 risk factors were found with zero disease event.
In [ ]: